Information Systems Lab

As we become more dependent upon computers, cyber-physical systems, and networked systems, the need to be able to operate in, and quickly recover from, adverse operating conditions becomes essential. Bad actors challenge these systems, resulting in a demonstrated need for systems which can detect, mitigate, and possibly initiate attacks. The performance of such systems must also be able to scale with an increasing complexity and frequency of attacks being encountered each hour of each day, which presents a challenge to systems providers in allowing for rapid event detection and protective responses across large-scale enterprises.

To achieve the above-mentioned goals, one must be able to process large quantities of sensor data and perform numerous correlations, all of which are needed to gain the confidence needed to determine whether a cyber-defender should act and when. To this end, cybersecurity has entered the era of big data, in which technologies that allow for the high-speed processing of large quantities of data are being explored to provide near-real time cyber situational awareness.

In general, this field involves the detection, identification, and mitigation of both malicious and non-malicious faults in complex systems and is an interdisciplinary study including a diverse set of disciplines within network and systems engineering, electrical engineering, computer science, and mathematics. Within this field of study, the Hume Center’s technical areas of interest include new cyber threat detection paradigms and technologies, large-enterprise cyber situational awareness, advanced persistent threat detection, anomaly detection and predictive data analytics, secure communications, hardware and software assurance, information assurance, secure computing, advanced computer programming languages, and secure power systems.

Core Capabilities

Hume Center research includes:

forecasting in complex systems;

scheduling and system optimization;

innovative machine learning techniques, such as deep learning;

machine learning to support patterns of life analysis and anomaly detection;